让机器像人一样灵活转换思维模式，实现跨领域抽象推理。
Oruga: An Avatar of Representational Systems Theory
- 基于表征系统理论构建数据结构与通信语言
- 通过结构迁移方法生成跨模态的表征变换
- 适合研究认知计算与人机协同智能的学者
人类能灵活运用表征：绘制图表、转换表达方式、在不同领域间建立创造性类比。我们希望将这种能力赋予机器，使其更契合人类使用。此前我们提出表征系统理论（RST）来研究表征的结构与转换机制。本文介绍Oruga（西班牙语意为毛虫，象征蜕变），一个实现RST多个方面的系统。Oruga包含对应于RST概念的核心数据结构、用于与核心交互的语言，以及基于结构迁移方法的转换引擎。本文概述Oruga的核心与语言，并简要展示结构迁移可执行的转换示例。
原文摘要 · Abstract (English)
Humans use representations flexibly. We draw diagrams, change representations and exploit creative analogies across different domains. We want to harness this kind of power and endow machines with it to make them more compatible with human use. Previously we developed Representational Systems Theory (RST) to study the structure and transformations of representations. In this paper we present Oruga (caterpillar in Spanish; a symbol of transformation), an implementation of various aspects of RST. Oruga consists of a core of data structures corresponding to concepts in RST, a language for communicating with the core, and an engine for producing transformations using a method we call structure transfer. In this paper we present an overview of the core and language of Oruga, with a brief example of the kind of transformation that structure transfer can execute.
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